qcop 0.9.2

A package for operating Quantum Chemistry programs using qcio standardized data structures. Compatible with TeraChem, psi4, QChem, NWChem, ORCA, Molpro, geomeTRIC and many more.

Quantum Chemistry Operate

A package for operating Quantum Chemistry programs using qcio standardized data structures. Compatible with TeraChem, psi4, QChem, NWChem, ORCA, Molpro, geomeTRIC and many more.

qcop works in harmony with a suite of other quantum chemistry tools for fast, structured, and interoperable quantum chemistry.

The QC Suite of Programs

• qcio - Elegant and intuitive data structures for quantum chemistry, featuring seamless Jupyter Notebook visualizations.
• qcparse - A library for efficient parsing of quantum chemistry data into structured qcio objects.
• qcop - A package for operating quantum chemistry programs using qcio standardized data structures. Compatible with TeraChem, psi4, QChem, NWChem, ORCA, Molpro, geomeTRIC, and many more, featuring seamless Jupyter Notebook visualizations.
• BigChem - A distributed application for running quantum chemistry calculations at scale across clusters of computers or the cloud. Bring multi-node scaling to your favorite quantum chemistry program, featuring seamless Jupyter Notebook visualizations.
• ChemCloud - A web application and associated Python client for exposing a BigChem cluster securely over the internet, featuring seamless Jupyter Notebook visualizations.

Installation

pip install qcop

Quickstart

qcop uses the qcio data structures to drive quantum chemistry programs in a standardized way. This allows for a simple and consistent interface to a wide variety of quantum chemistry programs. See the qcio library for documentation on the input and output data structures.

The compute function is the main entry point for the library and is used to run a calculation.

from qcio import Structure, ProgramInput
from qcop import compute
from qcop.exceptions import ExternalProgramError
# Create the Structure
h2o = Structure.open("h2o.xyz")

# Define the program input
prog_input = ProgramInput(
    structure=h2o,
    calctype="energy",
    model={"method": "hf", "basis": "sto-3g"},
    keywords={"purify": "no", "restricted": False},
)

# Run the calculation; will return ProgramOutput or raise an exception
try:
    po = compute("terachem", prog_input, collect_files=True)
except ExternalProgramError as e:
    # External QQ program failed in some way
    po = e.program_output
    po.input_data # Input data used by the QC program
    po.success # Will be False
    po.results # Any half-computed results before the failure
    po.traceback # Stack trace from the calculation
    po.ptraceback # Shortcut to print out the traceback in human readable format
    po.stdout # Stdout log from the calculation
    raise e
else:
    # Calculation succeeded
    po.input_data # Input data used by the QC program
    po.success # Will be True
    po.results # All structured results from the calculation
    po.stdout # Stdout log from the calculation
    po.pstdout # Shortcut to print out the stdout in human readable format
    po.files # Any files returned by the calculation
    po.provenance # Provenance information about the calculation
    po.extras # Any extra information not in the schema

One may also call compute(..., raise_exc=False) to return a ProgramOutput object rather than raising an exception when a calculation fails. This may allow easier handling of failures in some cases.

from qcio import Structure, ProgramInput
from qcop import compute
from qcop.exceptions import ExternalProgramError
# Create the Structure
h2o = Structure.open("h2o.xyz")

# Define the program input
prog_input = ProgramInput(
    structure=h2o,
    calctype="energy",
    model={"method": "hf", "basis": "sto-3g"},
    keywords={"purify": "no", "restricted": False},
)

# Run the calculation; will return a ProgramOutput objects
po = compute("terachem", prog_input, collect_files=True, raise_exc=False)
if not po.success:
    # External QQ program failed in some way
    po.input_data # Input data used by the QC program
    po.success # Will be False
    po.results # Any half-computed results before the failure
    po.traceback # Stack trace from the calculation
    po.ptraceback # Shortcut to print out the traceback in human readable format
    po.stdout # Stdout log from the calculation

else:
    # Calculation succeeded
    po.input_data # Input data used by the QC program
    po.success # Will be True
    po.results # All structured results from the calculation
    po.stdout # Stdout log from the calculation
    po.pstdout # Shortcut to print out the stdout in human readable format
    po.files # Any files returned by the calculation
    po.provenance # Provenance information about the calculation
    po.extras # Any extra information not in the schema

Alternatively, the compute_args function can be used to run a calculation with the input data structures passed in as arguments rather than as a single ProgramInput object.

from qcio import Structure
from qcop import compute_args
# Create the Structure
h2o = Structure.open("h2o.xyz")

# Run the calculation
output = compute_args(
    "terachem",
    h2o,
    calctype="energy",
    model={"method": "hf", "basis": "sto-3g"},
    keywords={"purify": "no", "restricted": False},
    files={...},
    collect_files=True
)

The behavior of compute() and compute_args() can be tuned by passing in keyword arguments like collect_files shown above. Keywords can modify which scratch directory location to use, whether to delete or keep the scratch files after a calculation completes, what files to collect from a calculation, whether to print the program stdout in real time as the program executes, and whether to propagate a wavefunction through a series of calculations. Keywords also include hooks for passing in update functions that can be called as a program executes in real time. See the compute method docstring for more details.

See the /examples directory for more examples.

✨ Visualization ✨

Visualize all your results with a single line of code!

First install the visualization module:

pip install qcio[view]

or if your shell requires '' around arguments with brackets:

pip install 'qcio[view]'

Then in a Jupyter notebook import the qcio view module and call view.view(...) passing it one or any number of qcio objects you want to visualizing including Structure objects or any ProgramOutput object. You may also pass an array of titles and/or subtitles to add additional information to the molecular structure display. If no titles are passed qcio with look for Structure identifiers such as a name or SMILES to label the Structure.

Seamless visualizations for ProgramOutput objects make results analysis easy!

Single point calculations display their results in a table.

If you want to use the HTML generated by the viewer to build your own dashboards use the functions inside of qcio.view.py that begin with the word generate_ to create HTML you can insert into any dashboard.

Support

If you have any issues with qcop or would like to request a feature, please open an issue.